Abstract

The neocortex is capable of modeling complex objects through sensorimotor interaction but the neural mechanisms are poorly understood. Grid cells in the entorhinal cortex represent the location of an animal in its environment, and this location is updated through movement and path integration. In this paper, we propose that grid-like cells in the neocortex represent the location of sensors on an object. We describe a two-layer neural network model that uses cortical grid cells and path integration to robustly learn and recognize objects through movement. Grid cells exhibit regular tiling over environments and are organized into modules, each with its own scale and orientation. A single module encodes position within the spatial scale of the module but is ambiguous over larger spaces. A set of modules can uniquely encode many large spaces. In our model, a layer of cells consisting of several grid-like modules represents a location in the reference frame of a specific object. Another layer of cells which processes sensory input receives this location input as context and uses it to encode the sensory input in the object's reference frame. Sensory input causes the network to invoke previously learned locations that are consistent with the input, and motor input causes the network to update those locations. Simulations show that the model can learn hundreds of objects even when object features alone are insufficient for disambiguation. We discuss the relationship of the model to cortical circuitry and suggest that the reciprocal connections between layers 4 and 6 fit the requirements of the model. We propose that the subgranular layers of cortical columns employ grid cell like mechanisms to represent object specific locations that are updated through movement.

Copyright

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